On the use of eddy-covariance and optical remote sensing data for biogeochemical modelling

On the use of eddy-covariance and optical remote sensing data for biogeochemical

modellingMarkus Reichstein, Dario Papale

Biogeochemical Model-Data-Integration Group, Max-Planck-Institute Jena

Laboratory of Forest Ecology, University of Tuscia

Carbon Fusion International Workshop Edinburgh, May 2006

BEAM-DIG

MPI-BGC

BEAM-DIG

MPI-BGC

Biogeochemical

Model-Data Integration Group

Carbon Fusion Workshop, Edinburgh May 2006 Markus Reichstein

Ecosystem models

+ provide system understanding+ promise inter-/extrapolation capacity+ may include historical effects

– are simplifications of the world– can’t predict stochastic events

Remote sensing

+ objective/consistent observations+ spatially and temporally dense

– data quality lower– processes not directly observable,

no history, no prediction

Ecosystem data

+ Potentially high quality+ often high temporal resolution

– data compatibility ? – ‘point’ observations

BGC-Model-Data Integration Overview


Outline

• Introduction to eddy covariance data

• Bottom-up perspective of an ‘ideal’ data integration-validation process

• Problems and obstacles in this process


Observing ecosystem gas exchange: eddy covariance

Flux = speed x concentration

Pho

to:

Bal

docc

hi


+ Measures whole ecosystem exchange of CO2 and H2O, …+ Non-destructive & continuous+ time-scale hourly to interannual+ integrates over large area

- only on flat sites- relies on turbulent conditions ==> data gaps, stochastic data- source area varying (flux footprint)- only ‚point‘ measurements

Does not deliver compartment fluxes, but:NEP = GPP - Reco

CO2, H2O

Eddy covariance


Half-hourly eddy covariance data

-25-20-15-10

-505

1015

5/2001 6/2001 7/2001 8/2001 9/2001 10/2001 10/2001 11/2001 12/2001 1/2002

Ca

rbo

n f

lux

[µm

ol m

-2 s

-1]

-20

30

80

130

180

230

280

5/2001 6/2001 7/2001 8/2001 9/2001 10/2001 10/2001 11/2001 12/2001 1/2002

Wate

r flu

x [W

m-2

]

0.00

0.20

0.40

0.60

0.80

1.00

1.20

5/2001 6/2001 7/2001 8/2001 9/2001 10/2001 10/2001 11/2001 12/2001 1/2002So

il w

ate

r co

nte

nt

[fra

ctio

n F

C]

Respiration

Carbon uptake

Evapotransp.


Network of ecosystem-level observations

>1000 site-years 1012 raw measurements (1013 bytes)

• Network and intercomparison studies• Harmonised and documented data processing

• Aubinet et al. (2000), Falge et al. (2001), Foken et al. (2002), Göckede/Rebmann/Foken (2004) : general set-up and methodology, quality assurance, gap-filling

• Reichstein et al. (2005), Glob. Ch. Biol.: u*-correction, gap-filling, partitioning of NEE

• Papale et al. (in prep), Biogeosciences: Quality control, eval. uncertainties• Moffat et al. (in prep): Gap-filling inter-comparison• Online processing tool: http://gaia.agraria.unitus.it/lab/reichstein/

Raw data Knowledge

1013 108 106 102 bytesTurb stat. Synth./aggr. Model param.


Ideal model-data integration cycle (bottom-up)

Model(re)formulation(Definition of model

structure)Model

characterization(Forward runs, consistency check,

sensitivity, uncert. analysis)

Model parameter estimation

(Multiple constraint)

Parameterinterpretation

(Thinking)

Generalization(‘up-scaling’)

Model validation(against indep. data, by scale or quantity)

Model application

DATA


The bottom-up model PROXEL

Canopy Layer 1Canopy Layer 2Canopy Layer 3

...

Canopy Layer n

Canopy

Solar radiation Air temperature [CO2] Relative humidity Wind speed

LAI, SAI

Leaf physiology

Phenology

CO2

H2O

Soil Layer 1Soil Layer 2Soil Layer 3

...

Soil Layer n

Soil

Air temperature Wind speed

Soil hydraulicparameters

Soil thermalparameters

Soil respirationparameters

PrecipitationWater

extraction

Vapour pressure

Root distri-bution

CO2

H2Oeffective soil

{Quantum use efficiency,electron transport and carboxylation capacities, stomatal conductance}


I. Model charaterization / forward model run

Rei

chst

ein,

Ten

hune

n et

al.,

Glo

bal C

hang

e B

iolo

gy, 2

002

CO

2 flu

x of

GP

P [

µm

ol m

-2 s

-1]

00.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

-2

0

2

4

6

8

10

12

0 4 8 12 16 20 24Local time [hr]

H2O

flux

[m

m/h

]

(a)

(c)

Eddy cov.Sap flowModelled

Eddy cov.Modelled

Well watered conditions

0 4 8 12 16 20 24Local time [hr]

(b)

(d)

Drought stressed conditions


II. Dual-constraint parameter estimation

Reic

hst

ein

et

al. 2

00

3, JG

R

Target region


IIa. Inferred parameter timeseries

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

150 200 250 300

Estimated course of photosynthetic capacity (Vcmax)

Rel

ativ

e ca

paci

ty

Rain event

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

150 200 250 300

Rel

ativ

e

Rain event

Julian day

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

150 200 250 300

Estimated course of photosynthetic capacity (Vcmax)

Rel

ativ

e ca

paci

ty

Rain event

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

150 200 250 300

Rel

ativ

e

Rain event

Julian day


0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 0.2 0.4 0.6 0.8 1 1.2

Rel

ativ

e va

lue

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0 0.2 0.4 0.6 0.8 1 1.2

Rel

ativ

e va

lue

Reichstein et al. 2003, JGR

III. Interpretation & Generalization

Relative soil water content

Rel

ativ

e le

af a

ctiv

ity


0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

ENF EBF DBF MF Sav Oshrub Crop

RU

E [

gC

/ M

J A

PA

R]

III. Interpretation and Generalization:Keyp. RUEmax

PFTs color coded

0 5 10

GPP_MOD17_MET [gC m-2 day-1]

0

5

10G

PP

[gC

m-2

day

-1]

0 5 10

0

5

10

Bi5/1

Bi7/1

Bi8/1Bi6/2Bi7/2Bi8/2

Bi9/2

Bi10/2

0 5 10

0

5

10

Bi1/1Bi2/1

Bi3/1Bi4/1

Bi5/1

Bi6/1Bi7/1

Bi8/1

Bi9/1Bi10/1

Bi11/1Bi12/1

0 5 10

0

5

10

Br1/1

Br2/1

Br3/1

Br4/1

Br5/1

Br6/1

Br7/1

Br8/1

Br9/1Br10/1Br11/1

Br12/1

0 5 10

0

5

10

El2/1

El3/1

El4/1

El5/1

El6/1

El8/1

El12/1El1/2

El6/2

El7/2

El8/2

El10/2El11/2El12/2

0 5 10

0

5

10

Ha1/1Ha2/1Ha3/1

Ha4/1

Ha5/1

Ha6/1

Ha7/1

Ha8/1

Ha9/1

Ha10/1

Ha11/1Ha12/1

0 5 10

0

5

10

He1/1 He2/1He3/1

He4/1

He5/1

He6/1He7/1

He8/1

He9/1

He10/1

He11/1

He12/1He1/2 He2/2

He3/2

He4/2

He5/2

He6/2He7/2

He8/2

He9/2

He10/2

He11/2He12/2

0 5 10

0

5

10

Hy1/1Hy3/1

Hy4/1

Hy5/1

Hy6/1

Hy7/1

Hy8/1

Hy9/1

Hy10/1

Hy11/1

0 5 10

0

5

10

Jo1/1Jo2/1Jo3/1Jo4/1Jo5/1

Jo6/1

Jo7/1

Jo8/1

Jo9/1

Jo10/1Jo11/1Jo12/1Jo1/2Jo2/2Jo3/2

Jo4/2

Jo5/2

Jo6/2

Jo7/2

Jo8/2

Jo9/2

Jo10/2Jo11/2Jo12/2

0 5 10

0

5

10

Mi1/1

Mi4/1

Mi6/1

Mi11/1

Mi2/2

Mi4/2

Mi7/2Mi9/2

0 5 10

0

5

10

No2/1

No3/1

No5/1

No6/1

No7/1

No8/1

No9/1

No10/1

No11/1No12/1

0 5 10

0

5

10

Pi4/2

Pi5/2

Pi6/2

Pi7/2

Pi8/2

Pi9/2

0 5 10

0

5

10

Pu1/1

Pu2/1

Pu3/1

Pu4/1Pu5/1

Pu6/1

Pu7/1

Pu8/1Pu9/1

Pu10/1Pu11/1

Pu12/1

0 5 10

0

5

10

Sa3/1

Sa4/1

Sa5/1Sa6/1

Sa7/1

Sa8/1

Sa9/1Sa10/1

Sa12/1

0 5 10

0

5

10

TC1/1TC2/1TC3/1

TC4/1

TC5/1

TC6/1

TC7/1

TC8/1

TC9/1

TC10/1

TC11/1

TC12/1TC1/2TC2/2

TC3/2 TC4/2

TC6/2

TC8/2

TC9/2

TC10/2

TC11/2TC12/2

0 5 10

0

5

10

Th1/1Th2/1

Th3/1

Th4/1

Th5/1

Th6/1

Th7/1

Th8/1

Th9/1

Th10/1

Th11/1

Th12/1

0 5 10

0

5

10

Vi1/1

Vi2/1

Vi3/1

Vi4/1

Vi6/1 Vi7/1

Vi8/1

Vi9/1

Vi10/1

Vi11/1

Vi12/1

0 5 10

0

5

10

Ya1/1

Ya2/1

Ya3/1

Ya6/1Ya8/1Ya9/1Ya10/1

Ya1/2

Ya2/2Ya3/2

Ya4/2

Ya5/2

Ya7/2 Ya8/2Ya9/2

Ya12/2

PFTs color coded

0 5 10

GPP_MOD17epsmax_new [gC m-2 d-1]

0

5

10G

PP

[gC

m-2

day

-1]

0 5 10

0

5

10

Bi5/1

Bi7/1

Bi8/1Bi6/2Bi7/2Bi8/2

Bi9/2

Bi10/2

0 5 10

0

5

10

Bi1/1Bi2/1

Bi3/1Bi4/1

Bi5/1

Bi6/1Bi7/1

Bi8/1

Bi9/1Bi10/1

Bi11/1Bi12/1

0 5 10

0

5

10

Br1/1

Br2/1

Br3/1

Br4/1

Br5/1

Br6/1

Br7/1

Br8/1

Br9/1Br10/1Br11/1

Br12/1

0 5 10

0

5

10

El2/1

El3/1

El4/1

El5/1

El6/1

El8/1

El12/1El1/2

El6/2

El7/2

El8/2

El10/2El11/2El12/2

0 5 10

0

5

10

Ha1/1 Ha2/1Ha3/1

Ha4/1

Ha5/1

Ha6/1

Ha7/1

Ha8/1

Ha9/1

Ha10/1

Ha11/1Ha12/1

0 5 10

0

5

10

He1/1 He2/1He3/1

He4/1

He5/1

He6/1He7/1

He8/1

He9/1

He10/1

He11/1

He12/1He1/2 He2/2

He3/2

He4/2

He5/2

He6/2He7/2

He8/2

He9/2

He10/2

He11/2He12/2

0 5 10

0

5

10

Hy1/1Hy3/1

Hy4/1

Hy5/1

Hy6/1

Hy7/1

Hy8/1

Hy9/1

Hy10/1

Hy11/1

0 5 10

0

5

10

Jo1/1Jo2/1Jo3/1 Jo4/1Jo5/1

Jo6/1

Jo7/1

Jo8/1

Jo9/1

Jo10/1Jo11/1Jo12/1Jo1/2Jo2/2Jo3/2

Jo4/2

Jo5/2

Jo6/2

Jo7/2

Jo8/2

Jo9/2

Jo10/2Jo11/2Jo12/2

0 5 10

0

5

10

Mi1/1

Mi4/1

Mi6/1

Mi11/1

Mi2/2

Mi4/2

Mi7/2Mi9/2

0 5 10

0

5

10

No2/1

No3/1

No5/1

No6/1

No7/1

No8/1

No9/1

No10/1

No11/1No12/1

0 5 10

0

5

10

Pi4/2

Pi5/2

Pi6/2

Pi7/2

Pi8/2

Pi9/2

0 5 10

0

5

10

Pu1/1

Pu2/1

Pu3/1

Pu4/1Pu5/1

Pu6/1

Pu7/1

Pu8/1Pu9/1

Pu10/1Pu11/1

Pu12/1

0 5 10

0

5

10

Sa3/1

Sa4/1

Sa5/1Sa6/1

Sa7/1

Sa8/1

Sa9/1Sa10/1

Sa12/1

0 5 10

0

5

10

TC1/1TC2/1TC3/1

TC4/1

TC5/1

TC6/1

TC7/1

TC8/1

TC9/1

TC10/1

TC11/1

TC12/1TC1/2TC2/2

TC3/2 TC4/2

TC6/2

TC8/2

TC9/2

TC10/2

TC11/2TC12/2

0 5 10

0

5

10

Th1/1Th2/1

Th3/1

Th4/1

Th5/1

Th6/1

Th7/1

Th8/1

Th9/1

Th10/1

Th11/1

Th12/1

0 5 10

0

5

10

Vi1/1

Vi2/1

Vi3/1

Vi4/1

Vi6/1 Vi7/1

Vi8/1

Vi9/1

Vi10/1

Vi11/1

Vi12/1

0 5 10

0

5

10

Ya1/1

Ya2/1

Ya3/1

Ya6/1Ya8/1Ya9/1Ya10/1

Ya1/2

Ya2/2Ya3/2

Ya4/2

Ya5/2

Ya7/2Ya8/2Ya9/2

Ya12/2

250.0500.0750.01000.01250.01500.01750.02000.0

0.00

2000.00

250.0500.0750.01000.01250.01500.01750.02000.0

0.00

2000.00

250.0500.0750.01000.01250.01500.01750.02000.0

0.00

2000.00

• inter-PFT variability• intra-PFT variability• f(species, N, T???)

IV. Validation at larger scale

70°N29,2° W

11° W 23° E

58° E

60°N

50°N

40°N

"Les Landes"

0

5

10

15

20

Flight 'Upscaled' Schmittgen et al. (2004), JGR

NE

E in

teg

rate

d 1

2:30

-14:

30[µ

mol

m-2

s-1]


GCB, in press


The problems


To consider with DA of eddy covariance data:

• How is the error structure of the data itself?

• How to address mismatch of scales (‘point’ versus pixel)?– Remote sensing– Meteorological data

• How do perform up-scaling from tower sites?– Representativity– Generalization


Errors in the data


Error model influence on parameter estimates

Const. abs errors Const. rel. errors

Pa

ram

ete

r es

tima

te

Search strategy

I

II

Simplified after Trudinger et al. (OPTIC)


Errors in eddy covariance data

• Random errors – ~ 30% for the half-hourly flux, (turbulences !)

• Systematic errors– can be largely controlled/avoided

• Selective systematic errors– Conditions where the theory does not apply:– Low turbulent conditions (night-time)– Advection→ good quality control necessary

→“Better few unbiased data, than a lot of biased data”

→Uncertainties: mean NEE > interannual variability


Characterization of the random error

cf. Richardson et al. (2006)


NEE

0 6 12 18 24

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

0 6 12 18 24

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

NEE_sigma

0 6 12 18 24

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

0 6 12 18 24

Jan

Feb

Mar

Apr

Jun

Jul

Aug

Sep

Oct

Nov

Dec

2468101214

0.00

15.00

-10-505101520

-13.0

20.0

NEE[µmol m-2 s-1]

NEE_sigma[µmol m-2 s-1]

Quantifying uncertaintie

s


Error distribution of eddy covariance data

-20.0-16.9-13.8-10.8 -7.7 -4.6 -1.5 1.5 4.6 7.7 10.8 13.8 16.9 20.0

Error NEE [umol m-2 s-1]

0.00

0.10

0.20

0.30

0.40

1 0 3 6 3 11 10 20 31 39 75115

329

1401

3133

1286

371

16866 44 39 20 15 9 5 5 1 1 0

Skewness KurtosisGaussian: 0 0

Laplace: 0 6Empirical: -0.08 15


Distribution of model error against eddy data

Chevalier et al. (in rev.)


PDF only 10am-3pm and Jun-Sep

-13.9-12.5-11.1 -9.7 -8.2 -6.8 -5.4 -4.0 -2.6 -1.2 0.2 1.6 3.1 4.5 5.9 7.3 8.7 10.1 11.5 12.9 14.4Variable

0.00

0.05

0.10

0.15

10 0 0 0 0

10 0

2

7 76

14

17 17

29

42

38 38

3435

27

16

14

5

34

2

0 0 0 0 0 0 0 0 0 01

-13.9-12.5-11.1 -9.7 -8.2 -6.8 -5.4 -4.0 -2.6 -1.2 0.2 1.6 3.1 4.5 5.9 7.3 8.7 10.1 11.5 12.9 14.4NEE error

0.00

0.05

0.10

0.15

10 0 0 0 0

10 0

2

7 76

14

17 17

29

42

38 38

3435

27

16

14

5

34

2

0 0 0 0 0 0 0 0 0 01

NEE error


More complicated error structures


Maximizing the likelihood?

P

j jP

jjN

i iobs

iippOBSxf

J1

2,

2

12

,

2 ˆ

2

1,

2

1

p

Bayesian approachCost function:

Trust in data Trust in apriori model parameters


Spatial representation problem I

• Does the tower site represent the grid cell of interest?

– 0.25-2km km for MODIS/SEAWIFS remote

sensing

– 30-100 km for meteorological fields

– 30-100 km for DGVMs, BGCs applied in

global context

Aerial photo

Spatial heterogeneity...

Landsat

MODIS

1 km


It‘s not always so bad...

TM3 coeff. of variation

TM 3,4,7 MODIS 1,2,7

Dinh et al., subm.


Spatial representation problem II

• Does the network of tower sites represent the spatial domain of interest or are there chances to generalize with scaling variables?


Day of the year

fAP

AR

[M

OD

IS-R

T)

We have to have up-scaling strategies


Conclusions

• Eddy covariance data contains a lot of interpretable information on both carbon and water cycle

• Inclusion of pools and fluxes for system understanding and for linking short and long time-scales necessary

• Major challenge within eddy data– Characterization of the error (random, bias)

– Scale and representativeness problem

– Interpret. & Generalization of site specific parameters

– Documentation of site dynamics, that may violate model structure (e.g., soil water, management)


Conclusions

Documents

On the use of eddy-covariance and optical remote sensing data for biogeochemical modelling